CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression

TL;DR

This paper introduces CAAL, a confidence-aware active learning framework designed to efficiently select samples for measuring complex atmospheric particle properties from noisy, heteroscedastic data, improving data collection strategies.

Contribution

The paper presents a novel active learning approach that decouples uncertainty estimation and incorporates aleatoric uncertainty to enhance sample selection in heteroscedastic regression tasks.

Findings

CAAL outperforms standard active learning methods in simulations and real data.

The decoupled training stabilizes uncertainty estimates in noisy conditions.

Dynamic weighting improves sample efficiency in high-cost data collection.

Abstract

Quantifying the impacts of air pollution on health and climate relies on key atmospheric particle properties such as toxicity and hygroscopicity. However, these properties typically require complex observational techniques or expensive particle-resolved numerical simulations, limiting the availability of labeled data. We therefore estimate these hard-to-measure particle properties from routinely available observations (e.g., air pollutant concentrations and meteorological conditions). Because routine observations only indirectly reflect particle composition and structure, the mapping from routine observations to particle properties is noisy and input-dependent, yielding a heteroscedastic regression setting. With a limited and costly labeling budget, the central challenge is to select which samples to measure or simulate. While active learning is a natural approach, most acquisition strategies rely on predictive uncertainty. Under heteroscedastic noise, this signal conflates reducible epistemic uncertainty with irreducible aleatoric uncertainty, causing limited budgets to be wasted in noise-dominated regions. To address this challenge, we propose a confidence-aware active learning framework (CAAL) for efficient and robust sample selection in heteroscedastic settings. CAAL consists of two components: a decoupled uncertainty-aware training objective that separately optimises the predictive mean and noise level to stabilise uncertainty estimation, and a confidence-aware acquisition function that dynamically weights epistemic uncertainty using predicted aleatoric uncertainty as a reliability signal. Experiments on particle-resolved numerical simulations and real atmospheric observations show that CAAL consistently outperforms standard AL baselines. The proposed framework provides a practical and general solution for the efficient expansion of high-cost atmospheric particle property databases.